library(tidyr)
library(dplyr)
library(ggplot2)
Data summary
summary(df)
## country year sex child
## Afghanistan: 38 1995 : 200 female:1900 Min. : 0.0
## Algeria : 38 1996 : 200 male :1900 1st Qu.: 15.0
## Angola : 38 1997 : 200 Median : 57.0
## Argentina : 38 1998 : 200 Mean : 441.8
## Azerbaijan : 38 1999 : 200 3rd Qu.: 233.2
## Bangladesh : 38 2000 : 200 Max. :25661.0
## (Other) :3572 (Other):2600
## adult elderly
## Min. : 0.0 Min. : 0.0
## 1st Qu.: 814.5 1st Qu.: 51.0
## Median : 2120.0 Median : 184.0
## Mean : 9683.1 Mean : 1116.9
## 3rd Qu.: 5723.2 3rd Qu.: 556.2
## Max. :731540.0 Max. :125991.0
##
Number of cases per sex
knitr::kable(
df %>%
rowwise() %>% mutate(total = sum(child, adult, elderly)) %>%
group_by(sex) %>% summarize(total_sum=sum(total), .groups = 'drop')
)
| female |
15656162 |
| male |
27062807 |
Number of cases per age per year
by_age_and_year <- df %>% group_by(year) %>%
summarize(child=sum(child), adult=sum(adult), elderly=sum(elderly), .groups = 'drop')
ggplot(by_age_and_year, aes(year, group=1)) +
geom_line(aes(y=child, colour="child")) +
geom_line(aes(y=adult, colour="adult")) +
geom_line(aes(y=elderly, colour="elderly")) +
labs(x = "Year", y = "Cases", color = "Legend")
